Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add langcare/langcare-mcp-fhir --skill renal-function-dashboardgit clone --depth 1 https://github.com/langcare/langcare-mcp-fhirWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/langcare/langcare-mcp-fhir/renal-function-dashboard)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/renal-function-dashboard"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/renal-function-dashboard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/renal-function-dashboard"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/renal-function-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.04544 |
| Opus 5.5 | $0.00046 | $0.01818 |
| Sonnet 5.5 | $0.00023 | $0.00909 |
| Haiku 4.5 | $0.00012 | $0.00454 |
Grade A, and why
renal-function-dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Renal Function Dashboard
Overview
Pull BUN, creatinine, eGFR, cystatin C, urine protein, urine albumin-to-creatinine ratio, and electrolytes (potassium, calcium, phosphorus, magnesium, bicarbonate). Stage CKD per KDIGO 2024 guidelines using GFR categories (G1-G5) and albuminuria categories (A1-A3). Track eGFR trajectory over time, calculate annual rate of decline, and flag rapid progression. Detect AKI superimposed on CKD. Assess electrolyte management adequacy by CKD stage. Flag medication dose adjustments needed and nephrotoxic drugs.
FHIR Resources Used
| Resource | Purpose | Key Fields |
|---|---|---|
| Observation | Renal labs, electrolytes, urinalysis | code, valueQuantity, effectiveDateTime, interpretation |
| Condition | CKD diagnosis, comorbidities | code, clinicalStatus, onsetDateTime |
| MedicationStatement | Medications needing renal adjustment | medicationCodeableConcept, status, dosage |
| Patient | Demographics for eGFR calculation | birthDate, gender, extension (race) |
Instructions
Step 1: Retrieve Patient Demographics
Tool: fhir_read
resourceType: "Patient"
id: "[patient-id]"
Extract: age, gender, race (from US Core race extension). These are inputs to the CKD-EPI 2021 equation (which removed the race variable) or CKD-EPI cystatin C equation if available.
Step 2: Pull Core Renal Function Labs
Tool: fhir_search
resourceType: "Observation"
queryParams: "patient=[patient-id]&code=2160-0,3094-0,33914-3,82810-3,76484-0,48642-3,69405-9,14959-1&_sort=-date&_count=50"
LOINC codes:
- 2160-0 = Serum Creatinine
- 3094-0 = BUN (Blood Urea Nitrogen)
- 33914-3 = eGFR by CKD-EPI (creatinine-based)
- 82810-3 = eGFR by CKD-EPI 2021 (without race)
- 76484-0 = eGFR by CKD-EPI cystatin C
- 48642-3 = Urine albumin/creatinine ratio (UACR)
- 69405-9 = Urine albumin/creatinine ratio (alternate)
- 14959-1 = Urine microalbumin
If eGFR not directly available, calculate from creatinine using CKD-EPI 2021:
- eGFR = 142 * min(Scr/kappa, 1)^alpha * max(Scr/kappa, 1)^(-1.200) * 0.9938^age * (1.012 if female)
- kappa = 0.7 (female), 0.9 (male); alpha = -0.241 (female), -0.302 (male)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 327 lines · 115 tokens per session scan A 87307c738b06
renal-function-dashboard is a skill published in the GitHub repository langcare/langcare-mcp-fhir (71 stars, last pushed 6mo ago), licensed MIT. It adds 115 tokens to every session and 4,544 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
renal-function-dashboard compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| renal-function-dashboardlangcare/langcare-mcp-fhir | Skill | 71 | 6mo ago | MIT | A | 115 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |